class_docstr: FairSeq Wav2Vec2 encoder module.
  
  Args:
      input_size: input dim
      output_size: dimension of attention
      w2v_url: url to Wav2Vec2.0 pretrained model
      w2v_dir_path: directory to download the Wav2Vec2.0 pretrained model.
      normalize_before: whether to use layer_norm before the first block
      finetune_last_n_layers: last n layers to be finetuned in Wav2Vec2.0
                              0 means to finetune every layer if freeze_w2v=False.
class_docstr_tok: ["FairSeq","Wav2Vec2","encoder","module",".","Args",":","input_size",":","input","dim","output_size",":","dimension","of","attention","w2v_url",":","url","to","Wav2Vec2.0","pretrained","model","w2v_dir_path",":","directory","to","download","the","Wav2Vec2.0","pretrained","model",".","normalize_before",":","whether","to","use","layer_norm","before","the","first","block","finetune_last_n_layers",":","last","n","layers","to","be","finetuned","in","Wav2Vec2.0","0","means","to","finetune","every","layer","if","freeze_w2v=False","."]
code: def output_size(self) -> int:
      return self._output_size
  
file_path: espnet/espnet2/asr/encoder/wav2vec2_encoder.py
imports: import contextlib
  import copy
  import logging
  import os
  from typing import Optional, Tuple
  import torch
  from filelock import FileLock
  from typeguard import check_argument_types
  from espnet2.asr.encoder.abs_encoder import AbsEncoder
  from espnet.nets.pytorch_backend.nets_utils import make_pad_mask
  from espnet.nets.pytorch_backend.transformer.layer_norm import LayerNorm
label_desc: Use image node_id 2 for calling the FairSeqWav2Vec2Encoder obj's underlying member method code with example usage: obj.output_size() and returns: self